In the image measurement process and machine vision applications, in order to determine the relationship between the three-dimensional geometric position of a point on the surface of a space object and its corresponding point in the image, a geometric model of camera imaging must be established. The parameters of the geometric model are the parameters of the camera. In most cases, these parameters need to be obtained through experiments and calculations. The process of solving the parameters is called camera calibration (or camcorder calibration).
For example, in an optical motion capture system, an optical image of a moving object is acquired using a multi-camera method. During the optical motion capture process, the tracking and positioning software adopts a computer multi-vision principle, and calculates the coordinates and direction of the point cloud in the three-dimensional capture space according to the matching relationship between the two-dimensional point clouds of the images as well as the relative position and orientation of the cameras. Based on the three-dimensional coordinates of the point clouds, the position and orientation of each rigid body in the motion space are solved by identifying the rigid-body structures bound to different locations of the moving object, and then the motion trajectory of the moving object in the motion space is determined. In order to accurately calculate the coordinates of point clouds and the motion postures of the rigid bodies in the three-dimensional capture space, the motion capture system needs to determine the state of all cameras and the positional relationship between the cameras before operating, and thus camera calibration is required.
Regardless of whether it is in optical motion capture, image measurement, or machine vision applications, the calibration of camera parameters is a very critical part. The accuracy of the calibration results and the stability of the algorithm directly affect the accuracy of the results produced through the operation of the camera. Moreover, the calibration accuracy of the camera may directly affect the capturing accuracy of the entire optical motion capturing system, and thus may have a limited error tolerance. For example, a small calibration error may result in a substantial deviation. Therefore, performing a desired camera-calibration process is a prerequisite for subsequent operation.
However, in the process of operating an optical motion capture system, the calibration of cameras may still have the following problems. First, the operating environment of the system constantly changes, for example, there may be a difference in temperature between morning and evening, and the change in the operating environment may affect the operating status of the cameras. That is, the internal parameters of the cameras may be affected. Second, the installation environment of the camera will inevitably experience vibration, which may lead to shifts of the cameras with respect to their initial installation position, thereby affecting the current position relation between the cameras. That is, the external parameters of the cameras may be affected. Third, in actual applications, it is impossible to recalibrate the system at any time because recalibration of the cameras at any time may waste a lot of time and also significantly reduce the operating fluency of the entire system.
The disclosed camera automatic calibration method and optical motion capture system are directed to solve one or more problems set forth above and other problems in the art.